Sub-optimal recursively feasible Linear Parameter-Varying predictive algorithm for semi-active suspension control

This study proposes an algorithm to enhance the comfort of passengers in a vehicle with semi-active suspensions. The vertical dynamics of the car are represented through a quasi-linear parameter-varying (qLPV) model. The scheme resides in solving a set-constrained model predictive control (MPC) prob...

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Vydané v:	IET control theory & applications Ročník 14; číslo 18; s. 2764 - 2775
Hlavní autori:	Morato, Marcelo Menezes, Normey-Rico, Julio Elias, Sename, Olivier
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 17.12.2020 Institution of Engineering and Technology
Predmet:	Automatic car comfort performance index control system synthesis electro‐rheological suspensions Engineering Sciences linear MPC linear quadratic control linear systems MPC optimisation function nonlinear simulations optimal control optimal controllers optimisation original complex nonlinear optimisation performance index predictive control qLPV scheduling parameters quadratic programming quasilinear parameter‐varying model recursive feasibility semiactive suspension control semiactive suspensions set‐constrained model predictive control problem Special Section: Emerging Trends in LPV-Based Control of Intelligent Automotive Systems sub‐optimal recursively feasible linear parameter‐varying predictive algorithm sub‐optimality resides suspensions (mechanical components) vertical dynamics comfort performance index electro-rheological suspensions linear MPC optimal controllers vertical dynamics original complex nonlinear optimisation semiactive suspensions recursive feasibility optimisation car nonlinear simulations sub-optimal recursively feasible linear parameter-varying predictive algorithm performance index semiactive suspension control quasilinear parameter-varying model QP qLPV scheduling parameters suspensions (mechanical components) optimal control control system synthesis linear systems sub-optimality resides quadratic programming set-constrained model predictive control problem MPC optimisation function predictive control linear quadratic control
ISSN:	1751-8644, 1751-8652
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Shrnutí:	This study proposes an algorithm to enhance the comfort of passengers in a vehicle with semi-active suspensions. The vertical dynamics of the car are represented through a quasi-linear parameter-varying (qLPV) model. The scheme resides in solving a set-constrained model predictive control (MPC) problem, embedding a comfort performance index to the MPC optimisation function. Its sub-optimality resides in the fact that the MPC is synthesised considering a frozen guess for the evolution of the qLPV scheduling parameters along the horizon. Assuming bounds on the variation rates of the qLPV scheduling parameters, the method enables a replacement of the original complex non-linear optimisation by a much simpler quadratic program (QP). This QP uses a Lyapunov-decreasing cost and set-based terminal ingredients, which guarantee that the domain of attraction of the controller is enlarged and that recursive feasibility can be maintained. The study ends with successful realistic non-linear simulations of a one-fifth-scaled car with electro-rheological suspensions, for which the proposed method is tested and compared with other optimal controllers (Linear Quadratic Regulator (LQR) and linear MPC). The results illustrate the overall good operation of the vehicle; the comfort of the passengers is substantially improved, as measured through time- and frequency-domain indexes.
ISSN:	1751-8644 1751-8652
DOI:	10.1049/iet-cta.2020.0592